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An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining

Author

Listed:
  • Hengyuan Ma

    (Shanghai Jiao Tong University)

  • Wei Liu

    (Shanghai Jiao Tong University)

  • Xionghui Zhou

    (Shanghai Jiao Tong University)

  • Qiang Niu

    (Shanghai Jiao Tong University)

  • Chuipin Kong

    (Shanghai Jiao Tong University)

Abstract

The demand for optimization of manufacturing processes rises as a reflection of the highly competitive market environment that requires shorter lead time and lower production costs. Although some approaches to milling process optimization have been developed based on analytical model using average cutting parameters, they are not available for complex workpieces when cutting parameters are time-varying and instantaneous cutting conditions need to be considered. In order to automate the optimization process and avoid costly machining tests, in this paper, an effective approach for parameters optimization of complex end milling process based on virtual machining is proposed. A computer-aided design (CAD)/computer-aided manufacturing (CAM) application is integrated for actual tool path generation and feedrate scheduling based on material removal rate. Then, a machining simulator based on octree and instantaneous force model is developed to evaluate feasibility of the given numerical control (NC) program, and the correctness of this simulator is verified by machining tests. The optimization process is controlled by the efficient global optimization method to find global optimal solution with fewer simulations and less computation time. During each iteration of the optimization process, NC programs are generated and evaluated automatically by the CAD/CAM application and the simulator, respectively. The effectiveness and efficiency of the proposed approach are proved by comparing the generated optimal solution (has reduced machining time and production cost) with the recommended cutting parameters from machining experts when machining an impeller.

Suggested Citation

  • Hengyuan Ma & Wei Liu & Xionghui Zhou & Qiang Niu & Chuipin Kong, 2020. "An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 967-984, April.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01489-6
    DOI: 10.1007/s10845-019-01489-6
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    References listed on IDEAS

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    1. Congbo Li & Lingling Li & Ying Tang & Yantao Zhu & Li Li, 2019. "A comprehensive approach to parameters optimization of energy-aware CNC milling," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 123-138, January.
    2. Mahdi S. Alajmi & Fawzan S. Alfares & Mohamed S. Alfares, 2019. "Selection of optimal conditions in the surface grinding process using the quantum based optimisation method," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1469-1481, March.
    3. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
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    Cited by:

    1. Jiyoung Jung & Kundo Park & Byungjin Cho & Jinkyoo Park & Seunghwa Ryu, 2023. "Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3623-3636, December.
    2. Roman Stryczek & Kamil Wyrobek, 2021. "Heuristic techniques for modelling machine spinning processes," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1189-1206, April.

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